Volume 11 Number 3
June 2014
Article Contents
Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2
Cite as: Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

  • Received: 2013-08-27
Fund Project:

This work was supported by Czech Science Foundation (No.GACR 13-08195S), the project Central Register of Research Intentions CEZ MSM0021630528 Security-oriented Research in Information Technology, the specific research (No. FIT-S-11-2), the project RVO: 67985815, the Technological agency of the Czech Republic (TACR) project V3C (No.TE01020415), and Grant Agency of the Czech Republic-GACR P103/13/08195S.

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Abstract Views (5604) PDF downloads (3034) Citations (0)

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Fund Project:

This work was supported by Czech Science Foundation (No.GACR 13-08195S), the project Central Register of Research Intentions CEZ MSM0021630528 Security-oriented Research in Information Technology, the specific research (No. FIT-S-11-2), the project RVO: 67985815, the Technological agency of the Czech Republic (TACR) project V3C (No.TE01020415), and Grant Agency of the Czech Republic-GACR P103/13/08195S.

Abstract: Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline, astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.

Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2
Citation: Pavla Bromová, Petr Škoda and Jaroslav Vážný. Classification of Spectra of Emission Line Stars Using Machine Learning Techniques. International Journal of Automation and Computing, vol. 11, no. 3, pp. 265-273, 2014. doi: 10.1007/s11633-014-0789-2
Reference (24)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return